| 0 |
| 1 |
| Ecosystem | 1 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Starting Price | — | $10/mo |
| Capabilities | 13 decomposed | 13 decomposed |
| Times Matched | 0 | 0 |
Defines a single-source-of-truth registry.json that declares all agents, subagents, contexts, and commands as composable components with metadata. The system uses a hierarchical agent architecture where primary orchestrators (OpenAgent, OpenCoder) delegate specialized tasks to subagents (TaskManager, CodeReviewer) through a registry lookup mechanism, enabling dynamic agent instantiation and capability routing without hardcoded dependencies.
Unique: Uses a declarative registry.json as the single source of truth for agent definitions, enabling agents to be discovered and composed dynamically at runtime rather than through hardcoded imports. The hierarchical delegation pattern (primary agents → subagents) is explicitly modeled in the registry with typed component categories (Agents, Subagents, Contexts, Commands), allowing the framework to enforce composition rules and validate agent relationships during installation.
vs alternatives: More maintainable than agent frameworks that require code changes to add new agents, and more flexible than monolithic agent designs because agents can be versioned, swapped, and composed independently through registry metadata rather than tight coupling.
Implements a workflow where agents first generate a detailed plan (broken down into discrete steps) before executing any code changes. The plan is presented to users for review and approval before execution proceeds, with built-in checkpoints that allow rejection, modification, or conditional execution of specific plan steps. This pattern is enforced through the command system and evaluation framework, which validates plan quality before allowing agent actions.
Unique: Enforces a mandatory planning phase before execution through the command system architecture, where agents must decompose tasks into discrete, reviewable steps before any code modifications occur. The approval gate is not a post-hoc safety layer but a first-class architectural pattern integrated into the agent execution flow, with explicit support for plan modification and conditional step execution.
vs alternatives: Provides stronger safety guarantees than agents that execute immediately with only post-execution rollback, because the plan is visible and modifiable before any changes take effect. More practical than purely autonomous agents because it acknowledges that human judgment is needed for complex decisions while still automating the planning and execution of approved actions.
Integrates with OpenRepoManager to provide agents with repository-wide capabilities including file operations, code search, and dependency analysis. The abilities system exposes these capabilities as callable functions that agents can invoke to interact with the repository. Abilities are registered and discoverable, allowing agents to understand what operations are available without hardcoding them. The integration enables agents to perform complex repository operations like refactoring, dependency updates, and cross-file modifications.
Unique: Exposes repository operations as discoverable, callable abilities that agents can invoke dynamically, rather than hardcoding repository access patterns in agent code. The abilities system allows agents to understand what operations are available and invoke them with appropriate parameters, enabling complex repository-wide operations.
vs alternatives: More flexible than agents that can only modify individual files because it enables repository-wide operations and cross-file modifications. More discoverable than hardcoded repository operations because abilities are registered and agents can query what's available.
Provides a compatibility layer that allows agents to work with multiple IDEs including VS Code and OpenCode, abstracting away IDE-specific implementation details. The system detects the active IDE and loads appropriate IDE-specific plugins and configurations. Agents can invoke IDE operations (file operations, editor commands, terminal execution) through a unified interface that works across IDEs. IDE-specific context and capabilities are loaded dynamically based on the detected IDE.
Unique: Implements a compatibility layer that abstracts IDE-specific details behind a unified interface, allowing agents to invoke IDE operations without knowing which IDE is active. IDE-specific plugins are loaded dynamically based on the detected IDE, enabling IDE-specific features without duplicating agent logic.
vs alternatives: More portable than IDE-specific agents because the same agent code works across multiple IDEs. More maintainable than duplicating agent logic for each IDE because the compatibility layer centralizes IDE-specific handling.
Provides an installation mechanism (install.sh) that allows users to select which components to install through configurable profiles (essential, standard, meta). The installer parses registry.json, resolves component dependencies, and deploys only the selected components. Different profiles can be used for different use cases (e.g., minimal installation for CI/CD, full installation for local development). Installation is idempotent and can be re-run to update components.
Unique: Uses configurable profiles to allow selective installation of components based on use case, rather than requiring all-or-nothing installation. Profiles are defined in the installer and can be combined with manual component selection, providing flexibility for different deployment scenarios.
vs alternatives: More flexible than monolithic installation because users can choose which components to install. More maintainable than manual component installation because dependencies are resolved automatically.
Generates and validates code across TypeScript, Python, Go, and Rust through language-specific subagents that understand each language's syntax, idioms, and testing frameworks. Each language has dedicated validation logic that checks generated code for correctness before execution, with automatic test generation and execution through the evaluation framework. The system uses language-specific context files and prompt variants to guide code generation toward idiomatic patterns.
Unique: Uses language-specific subagents paired with language-specific prompt variants and context files to generate idiomatic code rather than generic code that happens to be syntactically valid. The evaluation framework automatically generates and executes tests for each language using native testing frameworks, providing real validation that generated code works rather than relying on static analysis.
vs alternatives: More sophisticated than generic code generators that produce syntactically correct but non-idiomatic code, because it explicitly models language-specific patterns and validates through actual test execution. Supports multiple languages in a single framework without requiring separate tools for each language.
Deploys specialized CodeReviewer subagents that analyze generated code against configurable review criteria including style, performance, security, and architectural patterns. The review process is integrated into the evaluation framework and runs automatically after code generation, producing structured feedback that can block or request modifications to generated code. Review criteria are defined in context files and can be customized per project.
Unique: Implements code review as a first-class subagent in the agent hierarchy rather than as a post-processing step, allowing review feedback to directly influence code generation through iterative refinement. Review criteria are declaratively defined in context files and can be versioned alongside code, ensuring review standards evolve with the codebase.
vs alternatives: More integrated than external code review tools because it's part of the agent workflow and can trigger code regeneration, whereas external tools typically only report issues. More flexible than hardcoded linting rules because review criteria can be customized and updated without code changes.
Loads and manages context files that contain codebase patterns, architectural standards, and domain-specific knowledge, then injects this context into agent prompts to guide code generation toward consistency with existing code. The system uses a Model-View-Intent (MVI) pattern for context organization where context is structured as reusable, composable modules that can be selectively loaded based on the task at hand. Context loading is dynamic and respects component dependencies defined in the registry.
Unique: Uses the MVI (Model-View-Intent) pattern to structure context as composable, reusable modules that can be selectively loaded based on task requirements, rather than loading all context for every task. Context is declared in the registry with explicit dependencies, allowing the system to automatically resolve which context files are needed for a given task and load them in the correct order.
vs alternatives: More maintainable than embedding patterns in prompts because context is versioned separately and can be updated without changing agent code. More efficient than loading all available context because selective loading respects token limits and reduces noise in agent prompts.
+5 more capabilities
Generates single-line and multi-line code suggestions in real-time as developers type, using semantic indexing of the entire codebase to retrieve relevant type definitions, function signatures, and contextual patterns. The system maintains a 1M token context window (Pro/Team tiers) that enables suggestions informed by distant code definitions and cross-file dependencies, constructed via local codebase semantic search rather than simple token-based recency. Suggestions adapt to detected coding style on Pro/Team tiers through implicit pattern learning from recent edits.
Unique: 1M token context window with codebase-wide semantic indexing enables suggestions informed by distant code definitions and cross-file patterns, versus competitors (Copilot, Tabnine) that typically use fixed context windows (4K-32K tokens) or file-local context. Claimed 250ms latency suggests optimized retrieval pipeline, though indexing mechanism and performance at scale remain undisclosed.
vs alternatives: Larger context window than GitHub Copilot (8K-32K tokens) and faster latency than unnamed competitors (250ms vs 783ms claimed), enabling suggestions on large codebases with minimal typing delay; trade-off is cloud dependency and undisclosed free tier limitations.
Provides a separate chat interface supporting multiple LLM backends (GPT-4o, Claude 3.5 Sonnet, GPT-4, others) for conversational code assistance. Users attach files, reference recent edits, and trigger compiler diagnostic uploads; the system generates diffs and applies code changes directly to the editor. Model selection is per-conversation, and $5/month in credits (included in Pro/Team) covers external model API costs; overage pricing is undisclosed. Hotkey-driven workflow enables rapid context switching between inline completion and chat.
Unique: Multi-model chat interface with per-conversation model selection and integrated diff application, combined with compiler diagnostic auto-upload. Unlike Copilot Chat (single model per tier) or standalone ChatGPT, Supermaven Chat unifies multiple LLM backends in a single hotkey-driven workflow with direct editor integration for change application.
Supermaven scores higher at 71/100 vs OpenAgentsControl at 43/100. OpenAgentsControl leads on ecosystem, while Supermaven is stronger on adoption and quality.
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vs alternatives: Supports multiple LLM backends (GPT-4o, Claude 3.5 Sonnet) in one interface with included credits, whereas GitHub Copilot Chat is single-model per tier and requires separate ChatGPT subscription for model switching; trade-off is credit limits and unknown overage pricing.
Supermaven Chat can automatically upload compiler diagnostic messages (errors, warnings) alongside code context to provide error-aware suggestions and fixes. The mechanism is described as 'automatically uploading your code together with compiler diagnostic messages,' but specific language/compiler support and the upload trigger mechanism are undisclosed. This feature is Chat-only and not available in inline completion.
Unique: Automatic compiler diagnostic upload in Chat for error-aware suggestions, versus competitors (Copilot, Tabnine) that require manual error context or have limited diagnostic integration. Supermaven's approach reduces friction but with undisclosed language/compiler support.
vs alternatives: Automatic diagnostic upload reduces manual context-gathering compared to manual copy-paste; trade-off is undisclosed language support and unclear upload trigger mechanism.
Supermaven offers a 30-day free trial of the Pro tier ($10/month), providing full access to 1M token context window, largest model, style adaptation, and $5/month chat credits. No credit card is required to start the trial (implied), and trial conversion to paid is automatic after 30 days unless cancelled. Trial terms and auto-renewal policy are not explicitly detailed.
Unique: 30-day free trial of Pro tier with full feature access (1M context, largest model, chat credits), versus competitors (Copilot 2-month free trial, Tabnine free tier only) with different trial lengths and feature access. Supermaven's approach is generous but with undisclosed auto-renewal terms.
vs alternatives: Full Pro feature access during trial compared to limited free tier; trade-off is undisclosed auto-renewal policy and potential unexpected charges if not cancelled.
Supermaven requires internet connectivity and server-side inference; no offline mode or local inference capability is mentioned or available. All code completion requests are sent to Supermaven's backend servers for processing, and responses are returned over the network. This creates a hard dependency on network connectivity and Supermaven's service availability; if the service is down or network is unavailable, code completion is not available.
Unique: Supermaven has no offline mode or local inference capability; all processing is server-side. GitHub Copilot also requires server-side inference, but Tabnine offers local inference options for some use cases. Supermaven's lack of offline capability is a significant limitation for developers with connectivity constraints.
vs alternatives: Supermaven's server-side-only approach is comparable to GitHub Copilot; Tabnine offers local inference options, making Tabnine more suitable for offline work. Supermaven's lack of offline capability is a weakness vs. Tabnine.
Analyzes recent code edits and inferred coding patterns to adapt inline suggestions to match team conventions, naming patterns, and structural preferences. The mechanism is implicit (not explicit fine-tuning) and operates only on Pro/Team tiers, suggesting pattern learning from editor activity rather than explicit configuration. Free tier uses a single base model without personalization.
Unique: Implicit style adaptation via editor activity analysis without explicit configuration, versus competitors (Copilot, Tabnine) that require manual style guides or explicit fine-tuning. Supermaven's approach is transparent to the user but also non-configurable and undisclosed in mechanism.
vs alternatives: Requires no manual style configuration compared to tools requiring explicit style guides; trade-off is lack of transparency and inability to control or export learned styles.
Delivers code suggestions to the editor inline as the developer types, with a claimed baseline latency of 250ms from keystroke to suggestion display. The system uses a cloud inference backend and local editor plugin to minimize round-trip time. Latency claim is positioned against an unnamed competitor (783ms), but methodology is undisclosed and no independent verification is provided.
Unique: Claimed 250ms latency via optimized cloud inference pipeline and editor plugin architecture, versus competitors with higher latency (783ms unnamed baseline). Actual differentiation is undisclosed; mechanism may involve request batching, model quantization, or edge caching, but specifics are not public.
vs alternatives: Faster than unnamed competitor (250ms vs 783ms claimed); trade-off is cloud dependency and unverified latency claim with no SLA or performance guarantee.
Provides native editor extensions for VS Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), and Neovim, enabling inline suggestion rendering, hotkey-driven chat access, and compiler diagnostic integration directly within the editor. Each plugin variant is maintained separately and integrates with the editor's native autocomplete UI, keybinding system, and file context APIs.
Unique: Native plugins for three major editor ecosystems (VS Code, JetBrains, Neovim) with integrated chat and diff application, versus competitors (Copilot, Tabnine) that support broader editor ecosystems but with less deep integration in some cases. Supermaven's approach prioritizes depth over breadth.
vs alternatives: Deep integration with VS Code and JetBrains (native autocomplete UI, hotkey system) compared to web-based tools or lighter integrations; trade-off is limited editor support (no Sublime, Vim, Emacs) and undisclosed Neovim support details.
+5 more capabilities